جست‌وجوی استاد و پژوهشگر

Mayank R. Mehtaمشاهده پروفایل

استاد

• Neurophysics
• Synaptic Plasticity
• Learning and Memory
• +۳ مورد دیگر

Mayank R. Mehta is a Professor at the University of California, Los Angeles (UCLA), holding joint appointments in the Departments of Physics & Astronomy, Neurology, and Neurobiology. He is a member of the Brain Research Institute and the W. M. Keck Center for Neurophysics at UCLA. His research bridges experimental and theoretical neuroscience, focusing on how neuronal networks encode space-time, the role of brain rhythms in learning and memory, and the impact of sleep and virtual reality on neural dynamics. His recent publications highlight breakthroughs in understanding hippocampal spatiotemporal selectivity, dendritic activity during behavior, and the causal influence of visual cues on memory neurons. Notable findings include the discovery that dendrites generate ten times more spikes than neuronal cell bodies and the modulation of hippocampal theta rhythms in virtual reality. Research Themes: Neurophysics of spatial-temporal coding Dendritic contributions to learning Virtual reality and brain plasticity Neural oscillations in memory consolidation Key Collaborators: Bert Sakmann (Max Planck Florida Institute) Thomas Hahn (Bernstein Center Heidelberg/Mannheim) Maryam Ghorbani (UCLA) Mehta's lab at UCLA trains graduate and postdoctoral researchers in cutting-edge techniques combining hardware development, electrophysiological recordings, and biophysical modeling. His work has significant implications for treating learning and memory disorders like Alzheimer's disease.

• Neural circuit mechanisms
• Adaptive learning
• State-dependent decision-making
• +۴ مورد دیگر

Dr. Sabine Krabbe is a Group Leader at the German Center for Neurodegenerative Diseases (DZNE) in Bonn, Germany, where she leads research on neural circuit mechanisms underlying adaptive learning and state-dependent decision-making. Her work integrates neuroscience, molecular biology, and behavioral approaches to understand how internal states influence behavior and how these processes are disrupted in neurological disorders. Dr. Krabbe's research focuses on the interactions between midbrain circuits of the substantia nigra and ventral tegmental area with their output structures such as the striatum and amygdala. She investigates how these networks integrate internal states with environmental cues to produce appropriate behavioral responses. Her laboratory employs state-of-the-art techniques including deep-brain calcium imaging at single-cell resolution in mice, opto- and pharmacogenetic manipulations, anatomical tracings, and molecular approaches to characterize neural circuit elements in detail. Her recent publications reveal significant insights into amygdala interneuron plasticity during fear learning, brain-wide representational drift in memory consolidation, and the molecular mechanisms underlying Parkinson's disease progression. Her work demonstrates how activity patterns within specific neural circuits change in early stages of neurodegenerative diseases and how this dysfunction contributes to cognitive deficits and emotional disturbances. Dr. Krabbe is actively involved in the neuroscience community, organizing the BonnBrain Conference 2026 and sharing research through social media platforms. She has established herself as an emerging leader in the field of systems neuroscience with a particular focus on the neural basis of emotional states and decision-making processes.

Stefano Fusiمشاهده پروفایل

دانشیار

• Computational Neuroscience
• Theoretical Neuroscience
• Neural Circuit Modeling
• +۲ مورد دیگر

Stefano Fusi is an Associate Professor of Neuroscience at Columbia University's Vagelos College of Physicians and Surgeons, with joint affiliations at the Mortimer B. Zuckerman Mind Brain Behavior Institute and Kavli Institute. His laboratory focuses on computational modeling of neural circuits and neuromorphic engineering. Education PhD in Physics, Hebrew University of Jerusalem (1999) BS in Physics, Sapienza University of Rome (1992) Research Focus Fusi investigates how biological complexity supports neural computation through three primary domains: theoretical analysis of neural circuit dynamics, representational geometry in learning systems, and hardware implementations of brain-inspired algorithms. His work bridges machine learning, neurophysiology, and theoretical physics, emphasizing high-dimensional representations and memory optimization. Recent publications demonstrate consistent focus on neural coding principles across hippocampus, prefrontal cortex, and sensory systems, with innovations in modeling working memory, stress responses, and cross-species computational paradigms. Collaborations & Labs Leads an interdisciplinary laboratory collaborating with Columbia experimental neuroscientists, MIT engineers, and Stanford computational researchers to validate theoretical models. Current projects include neuromorphic hardware development and neural decoding of emotional states.

Jens S. Andersenمشاهده پروفایل

استاد

• Proteomics
• Quantitative Mass Spectrometry
• Cell Biology
• +۱۱ مورد دیگر

Jens S. Andersen is a Professor in the Department of Biochemistry and Molecular Biology at the University of Southern Denmark, where he leads research in Biomedical Mass Spectrometry and Systems Biology. His work is centered on the development and application of quantitative mass spectrometry and microscopy-based proteomics to study human cell biology, particularly the structure and function of organelles such as centrosomes, cilia, autophagosomes, and mitochondria. His research focuses on determining the protein composition and dynamic properties of cellular organelles, the roles of specific protein groups, and their contributions to biological processes and diseases. He investigates cell signaling mediated by post-translational modifications, especially within the DNA damage response, autophagy, and immune systems. His lab, the Jens S. Andersen Lab, is part of the Research Section of Biomedical Mass Spectrometry. The analysis of his recent publications reveals a strong interdisciplinary trend combining proteomics, structural biology, and cell signaling. His work spans cilia biology, RNA metabolism, DNA repair, and cancer mechanisms, with frequent use of advanced techniques like mass spectrometry, CRISPR, and live-cell imaging. The integration of systems biology approaches is evident across his research outputs. Professor, Department of Biochemistry and Molecular Biology, University of Southern Denmark Head of Research, Biomedical Mass Spectrometry and Systems Biology Principal Investigator, Jens S. Andersen Lab ORCID: 0000-0002-6091-140X While no specific scientific awards are mentioned in the provided texts, his extensive publication record in high-impact journals such as Science , Nature Communications , Molecular Cell , and EMBO Journal reflects significant scholarly contributions. He has supervised research projects and collaborated widely across Europe, though specific names of students are not listed. His research is supported by multiple ongoing projects, reflecting sustained funding and academic leadership. The Jens S. Andersen Lab operates at the intersection of proteomics and cell biology, contributing to fundamental understanding of organelle dynamics and disease mechanisms. The lab's work is highly collaborative, involving partnerships with groups in structural biology, RNA research, and cancer biology.

Giorgio Ascoliمشاهده پروفایل

استاد

• Computational Neuroanatomy
• Neuroinformatics
• Neuronal Morphology
• +۵ مورد دیگر

Giorgio Ascoli is a University Professor in the Department of Bioengineering at George Mason University, where he has been since 1997. He is the Founding Director of the Center for Neural Informatics, Structures, & Plasticity (CN3) and Founding Editor-in-Chief of the journal Neuroinformatics . His affiliations span computational neuroanatomy, neuroinformatics, and hippocampal modeling. Education : PhD in Biochemistry and Neuroscience (1996), Scuola Normale Superiore; MS in Chemistry and Biochemistry (1993), Pisa University; BS in Chemistry and Physics (1991), Scuola Normale Superiore. Dr. Ascoli investigates the relationship between brain structure, activity, and function from cellular to circuit levels. His research focuses on anatomically plausible neural networks to model mammalian brains, particularly the hippocampus, with implications for understanding human memory and consciousness . He pioneered computational neuroanatomy, developing tools like L-Neuron for neuronal shape modeling and curating NeuroMorpho.Org , a central repository for digitally reconstructed neurons. His recent publications emphasize neuronal classification , connectome analysis , and biologically informed machine learning . Awards include the 2012 Outstanding Faculty Award (Virginia), 2022 AIMBE fellowship , and 2023 Presidential Faculty Excellence Awards . He has mentored over 20 graduate students and postdoctoral fellows, with funding from NIH, NSF, DARPA, and private foundations. Scientific Contributions : Over 137 peer-reviewed articles, 4 patents, 2 authored/edited books, and leadership in NeuroMorpho.Org and Hippocampome. Grants : $20M+ in cumulative funding, including NIH R01s, NSF BRAIN EAGERs, and Burroughs-Wellcome Trust support. Labs : Leads the Computational Neuroanatomy Group within CN3, focusing on hippocampal modeling, neuronal morphology, and consciousness theories.

Leslie Valiantمشاهده پروفایل

استاد

• Complexity Theory
• Machine Learning
• Parallel Computation
• +۴ مورد دیگر

Leslie Valiant is the T. Jefferson Coolidge Professor of Computer Science and Applied Mathematics in Harvard University's School of Engineering and Applied Sciences, where he has held a faculty position since 1982. A foundational figure in theoretical computer science, his work bridges artificial and natural computational phenomena across multiple disciplines. His academic background includes education at: King's College, Cambridge Imperial College, London Ph.D. in Computer Science from Warwick University (1974) Valiant's research spans computational complexity , machine learning theory , parallel systems , and computational neuroscience . He pioneered the PAC (Probably Approximately Correct) learning framework that established computational learning theory as a rigorous field. His holographic algorithms work revealed deep connections between computational complexity and statistical physics, while his neuroidal model and evolvability theory provide computational explanations for cognitive processes and biological evolution. Current investigations focus on cortical computation primitives and knowledge infusion architectures. His publication trends show increasing integration of neuroscience with computational theory since 2010, with dominant themes in holographic computation (2006-2018), cortical modeling (2012-2018), and evolvability (2009-2017). The work consistently applies computational complexity analysis to biological and cognitive systems. Major recognitions include: Nevanlinna Prize (1986) for mathematical aspects of computer science Knuth Award (1997) for foundational algorithms contributions EATCS Award (2008) for theoretical computer science impact Turing Award (2010) for computational learning theory and complexity Fellowship in the Royal Society and National Academy of Sciences Valiant's research has been supported by NSF and international grants enabling cross-disciplinary work in computational neuroscience and evolutionary algorithms. While specific advisees aren't documented in source materials, his theoretical frameworks have shaped generations of researchers in machine learning and complexity theory. His current research group explores neuroidal architectures for cognitive computation, investigating how cortical circuits achieve robust information processing through in-circuit testing methodologies. Ongoing projects aim to identify fundamental computational primitives in neural systems and develop biologically inspired AI frameworks.

Denise Headمشاهده پروفایل

استاد

• Cognitive Aging
• Neuroimaging
• Alzheimer's Disease
• +۹ مورد دیگر

Denise Head is Professor of Psychological & Brain Sciences and Associate Chair at Washington University in St. Louis, with an additional appointment as Associate Professor in Radiology. Her research integrates cognitive neuroscience and neuroimaging to study cognitive aging and Alzheimer's disease. PhD, University of Memphis MS, University of Memphis BS, University of New Orleans Her research focuses on age-related cognitive changes and their neural underpinnings. Key areas include spatial navigation deficits in aging, the role of lifestyle factors (exercise, sleep, stress) in brain aging, and interventions to support cognitive function in older adults. She uses virtual reality, mobile eye-tracking, and neuroimaging techniques such as fMRI and DTI. The recent publications highlight a consistent trajectory in cognitive neuroscience and aging research, with emphasis on neuroimaging biomarkers, structural brain changes, and cognitive performance in normal and pathological aging. Her work bridges psychology, neurology, and radiology, contributing to early detection and understanding of Alzheimer's disease. Scientific Awards: No awards listed in the provided text. Dr. Head advises graduate students and leads a research lab focused on cognitive aging, though specific student names are not listed. Her lab investigates mediators of brain aging and develops methods to support spatial navigation in older adults. While specific grants are not mentioned, her ongoing research and recent publications suggest active external funding. She leads a research team in the Department of Psychological & Brain Sciences, utilizing advanced neuroimaging and behavioral methods to study aging and dementia. The lab integrates real-world and virtual experimental designs to understand spatial cognition and brain health in older populations.

Joshua D. Rabinowitzمشاهده پروفایل

استاد

• Cellular Metabolism
• Metabolomics
• Systems Biology
• +۵ مورد دیگر

Joshua D. Rabinowitz is a Professor of Chemistry and the Lewis-Sigler Institute for Integrative Genomics at Princeton University, where he also serves as Director of the Ludwig Princeton Branch. His research focuses on achieving a quantitative, comprehensive understanding of cellular metabolism, with applications in both basic science and medical research. Dr. Rabinowitz's research interests span multiple areas of metabolism and systems biology: Quantitative analysis of metabolic networks and regulation Metabolomics and measurement of metabolite concentrations and fluxes Cancer cell metabolism and therapeutic targeting Metabolic regulation in microbes (E. coli, Saccharomyces cerevisiae) Biofuel production (focusing on Clostridium acetobutylicum) Metabolic impact of pathogen infection (viral infection of human cells) His laboratory has developed innovative methods for measuring cellular metabolites using state-of-the-art mass spectrometry technology and approaches for quantitating metabolic fluxes through isotope-labeling data interpretation. Analysis of recent publications reveals a strong focus on NAD+ metabolism, cancer metabolism, metabolic adaptations in disease states, and the intersection of metabolism with immunology and neuroscience, particularly in areas like T cell metabolism, Alzheimer's disease, and cardiac function. Dr. Rabinowitz has received recognition as a Highly Cited Researcher by Web of Science, indicating significant impact in his field. He advises several graduate students and has mentored numerous alumni, including Michel I. Nofal, Edmundo Leiva III, and Sean Hackett. His research is supported by multiple programs including NIH NHGRI Training Program and QCB Graduate Program. The Rabinowitz Lab operates at the intersection of chemistry, biology, and computational science, with all projects involving a mix of biological experiments, metabolomics, and computation to achieve their goal of a holistic understanding of cellular metabolism.

Matthias Gruberمشاهده پروفایل

دانشیار

• Cognitive Neuroscience
• Memory
• Motivation
• +۳ مورد دیگر

Dr. Matthias Gruber is a Reader (Associate Professor) in Cognitive Neuroscience at Cardiff University's School of Psychology. He leads the Cardiff University Motivation and Memory Lab at CUBRIC, where his research investigates the neuroscience of motivation and its effects on memory using multimodal neuroimaging techniques (structural/functional MRI, M/EEG). His work focuses on how intrinsic motivational states like curiosity enhance learning and memory consolidation. Education includes: PhD in Cognitive Neuroscience, University College London (2007-2011) Research Assistant position at University of Regensburg (2003-2006) Research explores: Neural mechanisms of curiosity and motivation Effects of reward anticipation on memory encoding Hippocampal-prefrontal interactions during learning Developmental aspects of curiosity from childhood to adulthood Real-world applications of motivation research His publications consistently demonstrate how motivational states modulate memory systems, with recent work focusing on spatial exploration dynamics and educational applications. Awards and honors: Sir Henry Dale Fellowship (Wellcome/Royal Society, 2019-2024) Laird Cermak Award (2016) Michael S. Gazzaniga Prize for Cognitive Neuroscience (2015) Elected to Memory Disorders Research Society (2017) Research funding includes major grants from Wellcome Trust, Royal Society, and European Commission. He leads the Motivation and Memory Lab at CUBRIC and is currently accepting expressions of interest from potential research trainees.

Barbara Landauمشاهده پروفایل

استاد

• Cognitive Science
• Developmental Psychology
• Neuroscience
• +۳ مورد دیگر

Barbara Landau is the Dick and Lydia Todd Professor of Cognitive Science at Johns Hopkins University (since 2001). She previously served as Vice Provost for Faculty (2011-2014) and Director of the Science of Learning Institute (2013-2018). Her research focuses on the interplay between language and spatial cognition, studying typical and atypical development through experimental psychology, linguistic analysis, and brain imaging. PhD in Cognitive Science, University of Pennsylvania Landau investigates the cognitive primitives underlying early development, including how children learn spatial language (e.g., prepositions), how spatial impairments affect word learning in Williams syndrome, and how language and spatial cognition interact. Her work spans typical development, congenital blindness, Williams syndrome, and post-stroke spatial representation. She leads the Language and Cognition Lab, part of the JHU Vision Sciences Group (Cognitive Science, Psychological and Brain Sciences, Neuroscience). Her research has been featured in Time , New York Times , NPR, and The New Yorker, with a focus exhibit at the Walters Art Museum (2011). She received the Guggenheim Fellowship (2009), William James Fellow Award (2018), and is a member of the National Academy of Sciences. Cognitive Science Society Fellow American Academy of Arts and Sciences Fellow American Association for the Advancement of Science Fellow Landau’s lab collaborates with University of Pennsylvania researchers (John Trueswell, Lila Gleitman) on NSF-funded projects connecting symmetry to linguistic/perceptual development. She trains students like Zihan Wang (2023 Glushko Award) and Rennie Pasquinelli (Science of Learning Fellow). Her studies involve participants aged 18 months to 18 years, including Williams syndrome individuals.

Andrea Greveمشاهده پروفایل

مدرس

• Cognitive Psychology
• Memory Studies
• Neuroscience
• +۳ مورد دیگر

Dr Andrea Greve is a Lecturer in the Department of Psychology at the University of Cambridge . Her research focuses on cognitive processes related to memory, prediction error, and learning mechanisms. Key areas of interest include declarative memory formation, semantic predictions, and the influence of novelty on memory retention. She has explored topics such as word learning in variable-choice paradigms, the role of hippocampal lesions in memory binding, and predictive coding in neuroimaging contexts. Her work integrates experimental psychology with neuroscience methodologies, particularly leveraging neuroimaging techniques to investigate memory systems. Notable contributions include studies on false memory effects, the nonmonotonic relationship between object-location memory and expectedness, and the impact of prior knowledge on memory encoding. Dr. Greve has also contributed to methodological advancements, such as improved MRI anonymization for MEG coregistration. While her research spans multiple decades, recent efforts (2023–2025) emphasize predictive frameworks and their applications in understanding cognitive phenomena like semantic surprise and episodic memory formation. Her findings challenge traditional assumptions about fast mapping in adults and highlight the importance of integrating computational models with empirical data. Dr. Greve collaborates extensively with neuroimaging and cognitive science teams, contributing to interdisciplinary projects that bridge theoretical and applied research in memory systems. Her work maintains a strong focus on methodological rigor, particularly in experimental design and data interpretation.

• Neuroscience
• Cognitive Neuroscience
• Memory Processes
• +۴ مورد دیگر

Simon Hanslmayr is a Professor in the School of Psychology & Neuroscience at the University of Glasgow. His research investigates neural oscillations' role in attention and memory processes, employing EEG, fMRI, and transcranial stimulation techniques. He focuses on healthy populations and clinical conditions like Schizophrenia and PTSD. His lab develops tools like the Brain Time Toolbox for electrophysiological data analysis. Education: Ph.D. in Cognitive Neuroscience (not explicitly detailed in text) Research interests include understanding how precise neural timing via oscillations underpins cognitive functions. Key areas: hippocampal memory coding, theta phase synchronization in associative memory, and causal effects of rhythmic stimulation on memory plasticity. Recent articles highlight mechanisms linking theta oscillations to memory formation, thalamocortical interactions in perception, and hippocampal-neocortical coupling. His work bridges experimental and computational approaches to model memory dynamics. Grants: Sensory stimulation for memory impairment (BIAL Foundation, 2025-2026) EU-funded studies on neural oscillations and memory (2020-2021) Awards: None explicitly listed, but active grant recipient. Supervised students include Kiera Capstick, Eleonora Marcantoni, and others. Collaborates with researchers worldwide through lab affiliates and visiting scholars. Current work emphasizes scalable neurotechnologies for cognitive enhancement and memory rehabilitation. Labs/Teams: Leads the Memory & Oscillations Lab at the University of Glasgow, collaborating with institutions like the University of Zurich and Maastricht University on neuroimaging and clinical studies.

Mehmet Koyutürkمشاهده پروفایل

استاد

• Systems Biology
• Computational Genomics
• Network Biology
• +۴ مورد دیگر

Mehmet Koyutürk serves as the Andrew R. Jennings Professor in the Department of Computer and Data Sciences at Case Western Reserve University's Case School of Engineering, with additional affiliation as a Member of the Cancer Genomics and Epigenomics Program at the Case Comprehensive Cancer Center. His computational research bridges algorithm development with biological applications, focusing on network-structured data analysis to address complex biomedical challenges. Dr. Koyutürk earned his Ph.D. in Computer Science from Purdue University following B.S. and M.S. degrees in Electrical Engineering and Computer Engineering from Bilkent University. His primary research domains include high-throughput biological data analysis, systems/network biology methodologies, data mining algorithms, and scientific computing optimization, with particular emphasis on phosphorylation networks, genomic interactions, and multi-omics integration. Recent publication trends reveal expanding applications of his network science expertise into Alzheimer's disease phosphoproteomics, bipolar disorder biomarker discovery, and intimate partner violence analysis, while maintaining core contributions to graph neural networks and biological link prediction. His group actively develops open-source analytical tools like RokaiXplorer for phospho-proteomic data accessibility. Scientific Recognition Andrew R. Jennings Professorship Dr. Koyutürk leads multiple NIH-funded initiatives including R01-LM012980 for phosphoproteomics analysis, U01-CA198941 (BD2K program) for big network integration, and R01-LM011247 for GWAS enhancement, complemented by NSF CAREER Award CCF-0953195. He serves on the steering committee for CWRU's Systems Biology and Bioinformatics graduate programs and as Associate Editor for IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB), with extensive collaboration through Mark Chance's Center for Proteomics and Bioinformatics. His laboratory specializes in developing scalable algorithms for biological network analysis, currently advancing projects on kinase-substrate association prediction, co-phosphorylation network characterization in cancer, and network-based approaches to intimate partner violence data mining, with strong emphasis on translating computational methods into biomedical insights through open-source software dissemination.

Gagan Wigمشاهده پروفایل

دانشیار

• Brain Network Connectivity
• Cognitive Aging
• Alzheimer's Disease
• +۳ مورد دیگر

Gagan Wig is an Associate Professor at the University of Texas at Dallas, School of Behavioral and Brain Sciences, with adjunct appointments in Psychiatry at UT Southwestern Medical Center. He directs the Wig Neuroimaging Lab, focusing on brain network connectivity across lifespan, healthy/pathological aging, and brain health disparities. His research employs structural/functional MRI, DTI, and TMS to study large-scale brain networks and their relationship to memory and attention. Professional preparation includes: Post-doctoral Associate in Neurology, Washington University School of Medicine (2012) Post-doctoral Associate in Psychology, Harvard University (2009) Ph.D. in Cognitive Neuroscience, Dartmouth College (2006) B.S. in Behavioral Neuroscience, University of British Columbia (2001) Research explores how brain networks change during aging and how socioeconomic status moderates these changes. His work reveals that education protects against brain network decline and that participant diversity is crucial for advancing brain aging research. Publications focus on neuroimaging of lifespan changes, Alzheimer's biomarkers, socioeconomic impacts on brain health, and methodological innovations in network analysis. Recent work demonstrates how functional network organization supports cognition despite Alzheimer's pathology and how data quantity affects network segregation measures. Awards include the Understanding Human Cognition Scholar Award from James S. McDonnell Foundation (2006). He has received significant funding including a $2.9M NIH grant studying socioeconomic links to Alzheimer's susceptibility and DARPA support for novel data sonification approaches.

David Klindtمشاهده پروفایل

استادیار

• NeuroAI
• Computational Neuroscience
• Sensory Processing
• +۳ مورد دیگر

David Klindt is Assistant Professor at Cold Spring Harbor Laboratory, leading research at the intersection of biological systems and artificial intelligence. His lab investigates how brains process sensory information and generalize knowledge across contexts, studying neural representations to inspire robust AI models. Research combines computational neuroscience and machine learning to develop algorithms mimicking biological learning efficiency. Current projects examine latent computing in biological neural networks through dynamical systems frameworks, sparse coding principles in neural representations, and geometric organization in visual processing. His group develops methods for mechanistic interpretability, self-supervised learning identifiability, and compute-efficient inference. Recent publications analyze toroidal representations in grid cells, retinal feature detection, and Cryo-EM structure disentanglement. Dr. Klindt's work has been recognized through publications in Nature Communications, eLife, and NeurIPS. Before joining CSHL, he was a Machine Learning Research Scientist at Meta Reality Labs and postdoctoral researcher at Stanford University and NTNU. He holds a Ph.D. in Computational Neuroscience and Machine Learning from the University of Tübingen.